This paper evaluates different deep learning based depth estimation algorithms. We propose improvements for a state-of-the-art unguided depth completion method where the number of necessary parameters can be more than halved at unvarying accuracy. Based on the results of the depth estimation evaluation, we consider the performance of semantic segmentation methods. We investigate if the completion improves the accuracy of point cloud based segmentation. The results are compared to the segmentation accuracy using only measured sensor data. Moreover, we give a comparison to the segmentation based solely on predicted depths of a monocular camera. Here, we depict the differences in accuracy when a costly lidar sensor is economized. The results are further validated on a self-provided dataset recorded with the institute’s own test vehicle.
Analysis of Depth Estimation and Semantic Segmentation Algorithms for the Environment Perception of Automated Vehicles
Proceedings
2021-08-03
19 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Deutsch
Multi-sensor environment perception for automated vehicles with semantic evidential grid maps
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